Create README.md

ResNet18 classifier that predicts one of four viewpoints (back, front, left_side, right_side) for frames showing Spotted Bowerbird individuals (images should be pre-processed to remove the background)

README.md

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+---
+base_model:
+- microsoft/resnet-18
+pipeline_tag: image-classification
+tags:
+- ecology
+- birds
+- posture
+---
+# Bowerbird viewpoint classifier (ResNet18)
+
+- Task: classify each frame into one of four viewpoints:  
+  `["back", "front", "left_side", "right_side"]`
+- Base model: `torchvision.models.resnet18` with `weights="IMAGENET1K_V1"`
+- Input size: 224 × 224 (after cropping)
+- Preprocessing (training/eval):
+  - Resize to 256 px on the shorter side
+  - Train: RandomResizedCrop(224), RandomRotation(7°), ColorJitter
+  - Eval: CenterCrop(224)
+- Normalization:
+  - mean  = [0.485, 0.456, 0.406]
+  - std   = [0.229, 0.224, 0.225]
+- Checkpoint file: `Bbird_viewpoint_classifier.pth`
+- The checkpoint stores a **PyTorch `state_dict`** for ResNet18 with a final
+  linear layer of 4 outputs (one per viewpoint class).
+
+> This model is **not** generic. It is specific to the four viewpoint classes
+> listed above. The classification head must have 4 outputs, in the same
+> class order: `back`, `front`, `left_side`, `right_side`.
+
+## Usage
+
+```python
+import torch
+from torch import nn
+from torchvision.models import resnet18
+from huggingface_hub import hf_hub_download
+
+# Replace this with the actual repo id on the Hub if different
+repo_id = "sarequi/bowerbird-viewpoint-classifier"
+
+# Download checkpoint
+ckpt_path = hf_hub_download(
+    repo_id=repo_id,
+    filename="Bbird_viewpoint_classifier.pth",
+)
+
+# Rebuild the model architecture exactly as in training
+NUM_CLASSES = 4
+model = resnet18(weights="IMAGENET1K_V1")
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+
+# Load weights
+state_dict = torch.load(ckpt_path, map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+
+VIEWPOINT_CLASSES = ["back", "front", "left_side", "right_side"]